Smart Water Networks Research Articles

Requirements for water data ecosystems: results from a business ecosystem case study

This paper studies the factors that affect the emergence of water data ecosystems using a case study as a research method. The study is based on interviews conducted with partners in a comprehensive business ecosystem focused on the development of smart water network management. Eleven representatives from six private companies, the waterworks of a city, and three organizations that provide water supply management services for municipalities were interviewed. The paper presents analysis of the interview results focusing on the interviewees’ thoughts on the state of water data systems in Finland and on the factors that affect the emergence of water data ecosystems in Finland.The interview results indicate a clear need for water data ecosystems but also obstacles preventing their emergence. Inadequate understanding on the part of customer, a lack of water data, regulations, and underdeveloped agreements were seen to hinder the development of water data solutions. In addition to ecosystem development, the emergence of water data ecosystems requires investment and the development of water data solutions, solution concepts, and demonstrations to show the value of the ecosystem. The results show that ecosystems need a clear rationale and vision, effective management of water data sharing, and mechanisms to ensure the scalability of water data ecosystems.

From Radio to In-Pipe Acoustic Communication for Smart Water Networks in Urban Environments: Design Challenges and Future Trends

The smart management of water resources is an increasingly important topic in today’s society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. This facilitates a continuous assessment of water quality and the state of health of the pipeline infrastructure, enabling early detection of leaks and water contamination. Acoustic-wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable for replacing traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media for data communication. Existing works proposing in-pipe acoustic communication systems are promising, but a comparison between the different implementations and their performance has not yet been reported. This paper reviews existing works dealing with acoustic-based communication networks in real large-scale urban water supply networks. For this purpose, an overview of the characteristics, trends and design challenges of existing works is provided in the present work as a guideline for future research.

Analysis of the effects of COVID-19 restriction policies on drinking water consumption by smart water network data filtering

Abstract People's habits changed during the COVID-19 pandemic and the consequent containment policies, with numerous implications in all fields. In particular, restrictions had important consequences for drinking water consumption. The present work analyses this influence in the Soccavo district of Naples (Campania), in Italy, during the two periods of strongest restrictions in 2020: the national Lockdown (March 11–May 3) and the autumn Red Zone (November 16–December 6). A large amount of data, referred to single-household flowmeters connected to a Smart Water Grid acquisition system, was collected for the years 2019 (considered the average reference year) and 2020. The first step was the preliminary filtering of the data, by identification and elimination of anomalies and outliers, as well as anomalous annual patterns, through clustering and classification. The second step consisted of the comparison of the same meters in two consecutive years considering the daily and weekly average hourly patterns, the average daily patterns of midweek days, Saturdays, and Sundays, respectively, and the total daily volumes. The results are consistent with those in the literature. Some general trends in literature data were sought and pointed out in the present paper.

Optimal sensor placement for leak location in water distribution networks: A feature selection method combined with graph signal processing

Nowadays, real-time monitoring of smart water networks is essentially performed by Supervisory Control and Data Acquisition (SCADA) systems and through the use of sensors strategically positioned in the water distribution network (WDN). The purpose of sensor placement is to reasonably collect signals and thus improve the efficiency of subsequent leak diagnosis, while also limiting their deployment and operational costs. Most current sensor placement methods rely on well-calibrated hydraulic models for node selection and subsequent leak location evaluation. In this study, an efficient Optimal Sensor Placement (OSP) method for WDN is proposed, which avoids the reliance on the hydraulic model in the node selection step. It can be generalized as an optimization process, and the algorithm to solve the problem is a heuristic algorithm. We consider the user nodes in the WDN as features and propose a feature selection algorithm combined with graph signal processing. By this method, the importance of different nodes in the WDN is analyzed and the redundancy between different nodes is considered in the iterative selection process. A graph signal reconstruction method is applied to recover pressure data of all nodes using the data monitored by the selected nodes, and the relationship between the number of sensors and the reconstruction error is analyzed. The proposed method is tested on two benchmark networks of different sizes and types. A comparative analysis with other methods shows that the proposed OSP method can be easily extended to other WDNs. In addition, the proposed method achieves better monitoring results while selecting nodes quickly.

Enhancing Pipe-Break Early Warning in Smart Water Networks: Distinguishing Leaks from Water Uses

This research presents a denoising technique developed for enhancing the identification of newly developed and/or developing leaks by acoustic loggers in smart water networks. The key challenge addressed is the differentiation of leak-induced signals from signals originating from other sources, such as customer water use, pumps operations and environmental noise. A spectral subtraction-based denoising technique is adapted to process the acoustic waves measured daily using wireless accelerometers. A newly captured wave file can be filtered based on a reference wave file, either one with a known nonleak noise source or one measured in the past at the same location, to highlight the differences or the evolution of the signals over time. This technique enhances the robustness of automated alarms in identifying leaks in water networks.

State‐of‐the‐art review of leak diagnostic experiments: Toward a smart water network

AbstractEffective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management.This article is categorized under: Engineering Water > Methods Engineering Water > Sustainable Engineering of Water Science of Water > Water Extremes

Smart water network infrastructures

Abstract This paper presents a solution to convert the conventional water network into a smart water network (SWN). Scenarios are synthesized for water recycling inside buildings with less water consumed, minimizing the overall cost. Multiphysics modelling and simulation are conducted with Hysys and Ansys - Fluent in calculating the water flow quantity, pressure of the water network and velocity of water inside the pipe network. Four scenarios are synthesized and modelled for a selected mall building case study. These scenarios reduce the water consumption in the mall from 100% to 29.4%, with a cost-saving of more than 60%.

Active Air-borne noise suppression for pipe break early warning in smart water networks

Acoustic alarm systems using IoT (internet of things) smart sensors (e.g. accelerometers) for leak detection in water networks are rapidly gaining popularity. One of the issues for such systems is the false alarms which often occur as a result of signal contamination by environmental noise, especially by air-borne noise resulting in additional vibration. This research proposes a dual-sensor concept for active air-borne noise suppression in accelerometer signals that are excited by both fluid-borne noise (including the leak signal) and air-borne environmental noise. In the proposed arrangement, a microphone adjacent to the measurement station is used to measure the air-borne noise simultaneously to the accelerometer mounted to the pipe. Three methods have been applied in this paper: 1) using the non-coherent output power (NCOP) between the accelerometer and microphone signals; 2) using a Wiener filter; and 3) using adaptive least mean square (LMS) filters together with adaptive infinite impulse response (IIR) filters. Field tests have been carried out on a cast iron water main embedded in a water distribution system. A speaker was used to generate air-borne noise around the measurement station and to excite unwanted vibrations in the pipe. The results show that all three methods can effectively suppress air-borne noise from the accelerometer signals mounted to the pipe. A noise reduction of 30 dB can be achieved using the NCOP in the frequency domain or using the Wiener filter in the time domain. The adaptive filters are relatively less effective and the effectiveness reduces with the increase in bandwidth of the air-borne noise. However, the adaptive filters have the potential to be applied online for real-time processing.

Acoustic Signal Classification by Support Vector Machine for Pipe Crack Early Warning in Smart Water Networks

Uncontrolled pipe breaks are a challenge for water utilities all over the world. This paper describes a technique that enables pipe cracks to be identified at an early stage before they become uncontrolled breaks by utilizing a permanent acoustic monitoring system as part of a smart water network. Multiple acoustic features are selected and extracted from recorded wave files that are associated with proactive repair and uncontrolled pipe break events. The extracted acoustic features and the associated wave file labels (as either crack/leak noise or no crack/leak noise) are used to train a support vector machine model. The trained model has been operationalized in the South Australia Water Corporation’s smart water network analytics platform to process incoming new acoustic wave files in a near-real-time manner. If the acoustic wave file is classified as a pipe crack/leak, an alarm is sent to an investigation crew such that leak localization can be conducted and repairs started. The successful detection of multiple pipe cracks/leaks by the developed model after its implementation proves that it is an effective tool to enable proactive management of pipe breaks in water distribution systems.

An Advanced Boundary Protection Control for the Smart Water Network Using Semisupervised and Deep Learning Approaches

Critical infrastructures across many industries, such as smart water treatment and distribution networks (SWTDNs) and power generation and public transport networks, depend on the supervisory control and data acquisition (SCADA) system. However, being the core component of the critical infrastructures, it has made the SCADA-based SWTDN system an attractive target for cyberattacks. A successful attack on the SCADA will have a devastating impact on an SWTDN in terms of proper operations; therefore, safeguarding the SCADA from cyberattacks is of paramount. With the increasing cyberattacks on SWTDN, both in number and sophistication, the need to detect these attacks early has become a subject of great interest among practitioners and researchers. To this end, we propose a novel strategy, based on a semisupervised approach. Two semisupervised approaches, including unsupervised learning and deep learning-based approaches, have been proposed. The proposed approaches can involve learning dynamic cyberattack patterns from unlabeled data in an SWTDN. We validate the proposed semisupervised approach experimentally using an operational water treatment plant testbed. The proposed approach achieved almost 100% accuracy and substantially outperforms the existing baseline approaches used in this article. The outcome of the experiment is encouraging and demonstrates the potential use of the semisupervised approach for security control in smart water distribution.

A convolutional neural network for pipe crack and leak detection in smart water network

The implementation of a smart water network (SWN) is viewed as a strategic approach to address many challenges faced by water utilities, such as pipe leak detection and main break prevention. This paper develops a convolutional neural network (CNN)–based model to classify acoustic wave files collected by the South Australian Water Corporation’s (SA Water’s) SWN over the city of Adelaide. The VGGish model (VGG refers to the team who developed the model—Visual Geometry Group) is selected as a suitable transfer learning model to extract features from wave files. The CNN model classifies an acoustic wave file as an anomaly or other background or environmental noise. Identification of a wave file as an anomaly triggers a Siamese CNN model to determine whether it is related to a regular/irregular scheduled event (for example, irrigation system near public parks or water consumption by large buildings). A field investigation is initiated if a wave file is classified as an anomaly and it is not related to a scheduled event. The developed models have been validated using data that is recorded by SWN in Adelaide. This validation data set comprises 1098 wave files, which are recorded by 34 accelerometers and are associated with 32 known leaks. The validation results shown that accuracy of alarms generated by the developed models is 92.44%. The validations confirm the developed models as an effective tool for water pipeline leak and crack detection, which, in turn, enables proactive management of the pipeline assets.

Cyber-Physical Systems for Smart Water Networks: A Review

There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.

Pico turbines, the solution to self-supply energy to the water supply network. A case study in Las Palmas de Gran Canaria

This paper describes the integration of the pico turbines in the drinking water network of the city of Las Palmas de Gran Canaria (Spain).World's drinking water supply companies need updates his infrastructure and technology to turn existing networks into smart water networks, to preserve people's health by ensuring the potability of water for human consumption.The essential parameters for water quality (such as chlorine level, pH, turbidity and others) can be easily measured using sensors inserted in the pipes and in the storage and distribution tanks. Access to this information in real time and immediate action is essential to ensure the health of the people who access this vital element.It is therefore essential to increase the number of sensors and the analysis of the data they provide in order to detect anomalies, correct them and anticipate their consequences. Some of the objectives are the correct measurement of meters to detect leaks and anomalous consumption, saving water and energy, avoiding water and air pollution, achieving efficient supply to the consumer and achieving profitable, efficient and environmentally sustainable urban hydraulic systems.All systems require power, the pico turbines inserted in the network can supply power without affecting the normal operation of the network. Pico turbines are highly efficient, low cost and easy to install systems, but they are not widely used. The case exposed in this city could be replicated in many urban systems, it is novel that there is no literature of massive applications, existing a wide capacity of implantation and development of this technology.When it seems that the locations for the installation of hydroelectric plants have been exhausted, it is time to commit to the development of this mature technology on the micro-scale of the infrastructures already created, the novelty is to continue discovering those places where there is untapped energy potential.There is capacity to grow in very small hydroelectric systems with immediate profitability. The researchers in this article describe the first phase of an investigation that has gone hand in hand with its validation and actual exploitation. The aim is for managers not to forget this technology, which has a very wide growth field to satisfy the small demand for distributed energy in water networks of all types.The authors hope that the results of the research carried out will motivate other technicians to apply the energy potential of their water networks for the self-sufficiency of their control, remote control, chemical dosage and water analysis systems. The applications they carry out will be immediately profitable.

Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

Abstract Urban flooding is one of the major issues in many parts of the world, and its management is often challenging. One of the challenges highlighted by the hydrology and related communities is the need for more open data and monitoring of floods in space and time. In this paper, we present the development phases and experiments of an Internet of Things (IoT)-based wireless sensor network for hydrometeorological data collection and flood monitoring for the urban area of Colima-Villa de Álvarez in Mexico. The network is designed to collect fluvial water level, soil moisture and weather parameters that are transferred to the server and to a web application in real-time using IoT Message Queuing Telemetry Transport protocol over 3G and Wi-Fi networks. The network is tested during three different events of tropical storms that occurred over the area of Colima during the 2019 tropical cyclones season. The results show the ability of the smart water network to collect real-time hydrometeorological information during extreme events associated with tropical storms. The technology used for data transmission and acquisition made it possible to collect information at critical times for the city. Additionally, the data collected provided essential information for implementing and calibrating hydrological models and hydraulic models to generate flood inundation maps and identify critical infrastructure.

Performance Evaluation of Data Compression Algorithms for IoT-Based Smart Water Network Management Applications

IoT-based smart water supply network management applications generate a huge volume of data from the installed sensing devices which are required to be processed (sometimes in-network), stored and transmitted to a remote centre for decision making. When the volume of data produced by diverse IoT smart sensing devices intensify, processing and storage of these data begin to be a serious issue. The large data size acquired from these applications increases the computational complexities, occupies the scarce bandwidth of data transmission and increases the storage space. Thus, data size reduction through the use of data compression algorithms is essential in IoT-based smart water network management applications. In this paper, the performance evaluation of four different data compression algorithms used for this purpose is presented. These algorithms, which include RLE, Huffman, LZW and Shanon-Fano encoding were realised using MATLAB software and tested on six water supply system data. The performance of each of these algorithms was evaluated based on their compression ratio, compression factor, percentage space savings, as well as the compression gain. The results obtained showed that the LZW algorithm shows better performance base on the compression ratio, compression factor, space savings and the compression gain. However, its execution time is relatively slow compared to the RLE and the two other algorithms investigated. Most importantly, the LZW algorithm has a significant reduction in the data sizes of the tested files than all other algorithms

Assessment of Performance of Tesla Turbine in Water Distribution Systems for Energy Harvesting

Abstract This article composes a systematic design methodology to obtain optimal parameters of the Tesla turbine, which is applicable in water distribution systems, faced with the need to supply power for wireless sensors and other components used in smart water networks and the challenges associated with the feasibility of Tesla turbine in a small scale. The optimal geometric model is sought by using a theoretical model relating turbine physical properties and flow conditions to power output by solving Navier–Stokes equations for steady laminar incompressible flow between adjacent disks. The model shows that the proposed design with dimensions and flow conditions relevant to water pipe networks can achieve a power output of 1.55 mW using a 2.5-cm diameter turbine given a pressure difference of 140 Pa. At 70 Pa using a 1-cm diameter turbine, a power output of 0.17 mW can be obtained. This study shows a great potential in application of Tesla turbine for energy harvesting in water distribution systems.

Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems

Real-time control of pumps can be an infeasible task in water distribution systems (WDSs) because the calculation to find the optimal pump speeds is resource-intensive. The computational need cannot be lowered even with the capabilities of smart water networks when conventional optimization techniques are used. Deep reinforcement learning (DRL) is presented here as a controller of pumps in two WDSs. An agent based on a dueling deep q-network is trained to maintain the pump speeds based on instantaneous nodal pressure data. General optimization techniques (e.g., Nelder-Mead method, differential evolution) serve as baselines. The total efficiency achieved by the DRL agent compared to the best performing baseline is above 0.98, whereas the speedup is around 2x compared to that. The main contribution of the presented approach is that the agent can run the pumps in real-time because it depends only on measurement data. If the WDS is replaced with a hydraulic simulation, the agent still outperforms conventional techniques in search speed.

Water contaminants detection using sensor placement approach in smart water networks

Incidents of water pollution or contamination have occurred repeatedly in recent years, causing significant disasters and negative health impacts. Water quality sensors need to be installed in the water distribution system (WDS) to allow real-time water contamination detection to reduce the risk of water contamination. Deploying sensors in WDS is essential to monitor and detect any pollution incident at the appropriate time. However, it is impossible to place sensors on all nodes of the network due to the relatively large structure of WDS and the high cost of water quality sensors. For that, it is necessary to reduce the cost of deployment and guarantee the reliability of the sensing, such as detection time and coverage of the whole water network. In this paper, a dynamic approach of sensor placement that uses an evolutionary algorithm (EA) is proposed and implemented. The proposed method generates a multiple set of water contamination scenarios in several locations selected randomly in the WDS. Each contamination scenario spreads in the water networks for several hours, and then the proposed approach simulates the various effect of each contamination scenario on the water networks. On the other hand, the multiple objectives of the sensor placement optimization problem, which aim to find the optimal locations of the deployed sensors, have been formulated. The sensor placement optimization solver, which uses the EA, is operated to find the optimal sensor placements. The effectiveness of the proposed method has been evaluated using two different case studies on the example of water networks: Battle of the water sensor network (BWSN) and another real case study from Madrid (Spain). The results have shown the capability of the proposed method to adapt the location of the sensors based on the numbers and the locations of contaminant sources. Moreover, the results also have demonstrated the ability of the proposed approach for maximising the coverage of deployed sensors and reducing the time to detect all the water contaminants using a few numbers of water quality sensors.

Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures.

Monitoring contemporary water distribution networks (WDN) relies increasingly on smart metering technologies and wireless sensor network infrastructures. Smart meters and sensor nodes are deployed to capture and transfer information from the WDN to a control center for further analysis. Due to difficulties in accessing the water assets, many water utility companies employ battery-powered nodes, which restricts the use of high sampling rates, thus limiting the knowledge we can extract from the recorder data. To mitigate this issue, compressive sensing (CS) has been introduced as a powerful framework for reducing dramatically the required bandwidth and storage resources, without diminishing the meaningful information content. Despite its well-established and mathematically rigorous foundations, most of the focus is given on the algorithmic perspective, while the real benefits of CS in practical scenarios are still underexplored. To address this problem, this work investigates the advantages of a CS-based implementation on real sensing devices utilized in smart water networks, in terms of execution speedup and reduced ener experimental evaluation revealed that a CS-based scheme can reduce compression execution times around , while achieving significant energy savings compared to lossless compression, by selecting a high compression ratio, without compromising reconstruction fidelity. Most importantly, the above significant savings are achieved by simultaneously enabling a weak encryption of the recorded data without the need for additional encryption hardware or software components.

Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures

Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures